{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7eaf6879-60c8-48af-8bcb-12f28ac02d96",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2facc0b8-6915-49af-ab21-39c0d57a4fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def drawplt():\n",
    "    plt.figure()\n",
    "    plt.title('Cost and Income Of a Film')\n",
    "    plt.xlabel('Cost (Million Yuan)')\n",
    "    plt.ylabel('Income (Million Yuan)')\n",
    "    plt.axis([0, 25, 0, 60])\n",
    "    plt.grid(True)\n",
    "    X = [[6], [9], [12], [14], [16]]\n",
    "    y = [[9], [12], [29], [35], [59]]\n",
    "    plt.plot(X, y, 'k.')\n",
    "\n",
    "drawplt()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f94796d-eb5d-46c9-8c86-ac83b3fca1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def drawplt():\n",
    "    plt.figure()\n",
    "    plt.title('Cost and Income Of a Film')\n",
    "    plt.xlabel('Cost (Million Yuan)')\n",
    "    plt.ylabel('Income (Million Yuan)')\n",
    "    plt.axis([0, 25, 0, 60])\n",
    "    plt.grid(True)\n",
    "    X = [[6], [9], [12], [14], [16]]\n",
    "    y = [[9], [12], [29], [35], [59]]\n",
    "    plt.plot(X, y, 'k.')\n",
    "    \n",
    "    # Perform linear regression\n",
    "    regr = linear_model.LinearRegression()\n",
    "    regr.fit(X, y)\n",
    "    \n",
    "    # Plot the regression line\n",
    "    plt.plot(X, regr.predict(X), color='blue', linewidth=3)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "drawplt()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9aa4a64b-2b1f-44c6-9f23-9729f0ef4fbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "投资2千万的电影预计票房收入为：69.95百万元\n",
      "回归模型的系数是： [[4.78481013]]\n",
      "回归模型的截距是： [-25.74683544]\n",
      "最佳拟合线: y =  -25 ＋ 4 × x\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_33620\\386771554.py:23: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  print(\"最佳拟合线: y = \", int(b), \"＋\", int(w), \"× x\")\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (2, 1) + inhomogeneous part.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m     plt\u001b[38;5;241m.\u001b[39mplot([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m25\u001b[39m], [b, \u001b[38;5;241m25\u001b[39m \u001b[38;5;241m*\u001b[39m w \u001b[38;5;241m+\u001b[39m b])\n\u001b[0;32m     27\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m---> 29\u001b[0m \u001b[43mdrawplt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[4], line 26\u001b[0m, in \u001b[0;36mdrawplt\u001b[1;34m()\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m最佳拟合线: y = \u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mint\u001b[39m(b), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m＋\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mint\u001b[39m(w), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m× x\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     25\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(X, y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 26\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     27\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32mD:\\python3.11.8\\Lib\\site-packages\\matplotlib\\pyplot.py:3590\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3582\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[0;32m   3583\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m   3584\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3588\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   3589\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[1;32m-> 3590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3591\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3593\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3594\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3595\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3596\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\python3.11.8\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1724\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1481\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1482\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1483\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1721\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1722\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1723\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1724\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m   1725\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m   1726\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[1;32mD:\\python3.11.8\\Lib\\site-packages\\matplotlib\\axes\\_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\python3.11.8\\Lib\\site-packages\\matplotlib\\axes\\_base.py:489\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(xy) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m    488\u001b[0m     x \u001b[38;5;241m=\u001b[39m _check_1d(xy[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m--> 489\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxy\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    490\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    491\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m index_of(xy[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n",
      "File \u001b[1;32mD:\\python3.11.8\\Lib\\site-packages\\matplotlib\\cbook.py:1358\u001b[0m, in \u001b[0;36m_check_1d\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1352\u001b[0m \u001b[38;5;66;03m# plot requires `shape` and `ndim`.  If passed an\u001b[39;00m\n\u001b[0;32m   1353\u001b[0m \u001b[38;5;66;03m# object that doesn't provide them, then force to numpy array.\u001b[39;00m\n\u001b[0;32m   1354\u001b[0m \u001b[38;5;66;03m# Note this will strip unit information.\u001b[39;00m\n\u001b[0;32m   1355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m   1356\u001b[0m         \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m   1357\u001b[0m         \u001b[38;5;28mlen\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m-> 1358\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43matleast_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1359\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1360\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[1;32mD:\\python3.11.8\\Lib\\site-packages\\numpy\\core\\shape_base.py:65\u001b[0m, in \u001b[0;36matleast_1d\u001b[1;34m(*arys)\u001b[0m\n\u001b[0;32m     63\u001b[0m res \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ary \u001b[38;5;129;01min\u001b[39;00m arys:\n\u001b[1;32m---> 65\u001b[0m     ary \u001b[38;5;241m=\u001b[39m asanyarray(ary)\n\u001b[0;32m     66\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ary\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m     67\u001b[0m         result \u001b[38;5;241m=\u001b[39m ary\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (2, 1) + inhomogeneous part."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def drawplt():\n",
    "    plt.figure()\n",
    "    plt.title('Cost and Income Of a Film')\n",
    "    plt.xlabel('Cost(Million Yuan)')\n",
    "    plt.ylabel('Income(Million Yuan)')\n",
    "    plt.axis([0, 25, 0, 60])\n",
    "    plt.grid(True)\n",
    "    \n",
    "    X = [[6], [9], [12], [14], [16]]\n",
    "    y = [[9], [12], [29], [35], [59]]\n",
    "    \n",
    "    model = linear_model.LinearRegression()\n",
    "    model.fit(X, y)\n",
    "    a = model.predict([[20]])\n",
    "    w = model.coef_\n",
    "    b = model.intercept_\n",
    "    print(\"投资2千万的电影预计票房收入为：{:.2f}百万元\".format(model.predict([[20]])[0][0]))\n",
    "    print(\"回归模型的系数是：\", w)\n",
    "    print(\"回归模型的截距是：\", b)  \n",
    "    print(\"最佳拟合线: y = \", int(b), \"＋\", int(w), \"× x\")\n",
    "    \n",
    "    plt.plot(X, y, 'k.')\n",
    "    plt.plot([0, 25], [b, 25 * w + b])\n",
    "    plt.show()\n",
    "\n",
    "drawplt()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "553c5604-f061-4749-bfea-35bdda4b6107",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
